Importance Sampling Tree for Large-scale Empirical Expectation
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In this paper, we consider model combination methods for adaptive filtering that perform unbiased estimation. In this widely studied framework, two adaptive filters are run in parallel, each producing unbiased estimates of an underlying linear model. The o ...
This paper presents a new paradigm for choice set generation in the context of route choice. We assume that the choice sets contain all paths connecting each origin-destination pair. These sets are in general impossible to generate explicitly. Therefore, w ...
This thesis proposes a novel unified boosting framework. We apply this framework to the several face processing tasks, face detection, facial feature localisation, and pose classification, and use the same boosting algorithm and the same pool of features ( ...
Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. Due to vulnerability of the system to environmental noise and variation caused by the user, fusion of several biometric-enab ...
This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin-destination pair. Although this is behaviorally questionable, we make this ...
Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...
Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the t ...
Integral observers are useful tools for estimating the plant states in the presence of non-vanishing disturbances resulting from plant-model mismatch and exogenous disturbances. It is well known that these observers can eliminate bias in all states, given ...
We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten char- acter recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output ...
We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten character recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output l ...
